The disclosure generally relates to an advertisement audience detection device and, more particularly, to an advertisement audience dynamical detection circuit for estimating the quantity of out-of-home (OOH) advertisement audiences passing through a specific location in a specific time period, and related computer program product and method.
As commercial activities grow, various out of home (OOH) advertisements have become more and more popular. For example, plenty of OOH advertisements presented in the form of neon lamps, electronic displays, light boxes, balloons, large inflatable models, flat posters, or other formats can be easily found in various streets, parks, airports, public transportation vehicles, airships, or on the wall of corridors in various buildings.
For advertisers and advertisement agents, the quantity of advertisement audiences is one of the most concerned evaluation factors regarding the effects of advertising. But how to estimate the quantity of advertisement audiences is a very complex issue. The most traditional approach for estimating the quantity of audiences of OOH advertisement is to send personnel to stand beside the OOH advertisement so as to observe, count, and record the number of people watching the OOH advertisement. This traditional approach, however, demands too much labor cost and thus is not realistic to be applied for the situation where the advertisements are deployed in many different locations.
One of the newly developed approaches for estimating the quantity of audiences of OOH advertisement is to install cameras and a computer in the location of OOH advertisement so as to utilize image recognition technology to real-time detect the face or appearance features of the advertisement audiences in order to count and record the number of people watching the OOH advertisement. However, this approach requires the use of cameras, computers, and complex image recognition algorithms, but the detection range (or angle) of the camera is very limited. Therefore, this approach not only demands considerable hardware cost, but also results in poor detection accuracy. In addition, such approach is not suitable to be applied for the situation where the advertisements are deployed in many different locations.